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   第零章：前置知识
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     模型评价指标
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     常用包的学习
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    <a class="reference internal" href="../%E7%AC%AC%E9%9B%B6%E7%AB%A0/0.4%20Jupyter%E7%9B%B8%E5%85%B3%E6%93%8D%E4%BD%9C.html">
     Jupyter notebook/Lab 简述
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   第一章：PyTorch的简介和安装
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     1.1 PyTorch简介
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     1.2 PyTorch的安装
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     1.3 PyTorch相关资源
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   第二章：PyTorch基础知识
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     2.1 张量
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     2.2 自动求导
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     2.3 并行计算简介
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     AI硬件加速设备
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   第三章：PyTorch的主要组成模块
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     3.1 思考：完成深度学习的必要部分
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     3.2 基本配置
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     3.3 数据读入
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     3.4 模型构建
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     3.5 模型初始化
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     3.6 损失函数
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     3.7 训练和评估
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     3.8 可视化
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     3.9 PyTorch优化器
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   第四章：PyTorch基础实战
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     4.1 ResNet
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     基础实战——FashionMNIST时装分类
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   第五章：PyTorch模型定义
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     5.1 PyTorch模型定义的方式
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     5.2 利用模型块快速搭建复杂网络
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     5.3 PyTorch修改模型
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     5.4 PyTorch模型保存与读取
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   第六章：PyTorch进阶训练技巧
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     6.1 自定义损失函数
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     6.2 动态调整学习率
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     6.3 模型微调-torchvision
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     6.3 模型微调 - timm
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     6.4 半精度训练
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     6.5 数据增强-imgaug
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    <a class="reference internal" href="../%E7%AC%AC%E5%85%AD%E7%AB%A0/6.6%20%E4%BD%BF%E7%94%A8argparse%E8%BF%9B%E8%A1%8C%E8%B0%83%E5%8F%82.html">
     6.6 使用argparse进行调参
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   第七章：PyTorch可视化
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     7.1 可视化网络结构
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     7.2 CNN可视化
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     7.3 使用TensorBoard可视化训练过程
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     7.4 使用wandb可视化训练过程
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   第八章：PyTorch生态简介
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     8.1 本章简介
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     8.2 torchvision
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   7.4.1 wandb的安装
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   7.4.2 wandb的使用
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                <h1>7.4 使用wandb可视化训练过程</h1>
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  <section class="tex2jax_ignore mathjax_ignore" id="wandb">
<h1>7.4 使用wandb可视化训练过程<a class="headerlink" href="#wandb" title="永久链接至标题">#</a></h1>
<p>在上一节中，我们使用了Tensorboard可视化训练过程，但是Tensorboard对数据的保存仅限于本地，也很难分析超参数不同对实验的影响。wandb的出现很好的解决了这些问题，因此在本章节中，我们将对wandb进行简要介绍。
wandb是Weights &amp; Biases的缩写，它能够自动记录模型训练过程中的超参数和输出指标，然后可视化和比较结果，并快速与其他人共享结果。目前它能够和Jupyter、TensorFlow、Pytorch、Keras、Scikit、fast.ai、LightGBM、XGBoost一起结合使用。</p>
<p>经过本节的学习，你将收获：</p>
<ul class="simple">
<li><p>wandb的安装</p></li>
<li><p>wandb的使用</p></li>
<li><p>demo演示</p></li>
</ul>
<section id="id1">
<h2>7.4.1 wandb的安装<a class="headerlink" href="#id1" title="永久链接至标题">#</a></h2>
<p>wandb的安装非常简单，我们只需要使用pip安装即可。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">wandb</span>
</pre></div>
</div>
<p>安装完成后，我们需要在<a class="reference external" href="https://wandb.ai/">官网</a>注册一个账号并复制下自己的API keys，然后在本地使用下面的命令登录。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">wandb</span> <span class="n">login</span>
</pre></div>
</div>
<p>这时，我们会看到下面的界面，只需要粘贴你的API keys即可。
<img alt="" src="../_images/wandb_api_keys.png" /></p>
</section>
<section id="id2">
<h2>7.4.2 wandb的使用<a class="headerlink" href="#id2" title="永久链接至标题">#</a></h2>
<p>wandb的使用也非常简单，只需要在代码中添加几行代码即可。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">wandb</span>
<span class="n">wandb</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">project</span><span class="o">=</span><span class="s1">&#39;my-project&#39;</span><span class="p">,</span> <span class="n">entity</span><span class="o">=</span><span class="s1">&#39;my-name&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>这里的project和entity是你在wandb上创建的项目名称和用户名，如果你还没有创建项目，可以参考<a class="reference external" href="https://docs.wandb.ai/quickstart">官方文档</a>。</p>
</section>
<section id="demo">
<h2>7.4.3 demo演示<a class="headerlink" href="#demo" title="永久链接至标题">#</a></h2>
<p>下面我们使用一个CIFAR10的图像分类demo来演示wandb的使用。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>
<span class="kn">import</span> <span class="nn">random</span>  <span class="c1"># to set the python random seed</span>
<span class="kn">import</span> <span class="nn">numpy</span>  <span class="c1"># to set the numpy random seed</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
<span class="kn">from</span> <span class="nn">torchvision.models</span> <span class="kn">import</span> <span class="n">resnet18</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>使用wandb的第一步是初始化wandb，这里我们使用wandb.init()函数来初始化wandb，其中project是你在wandb上创建的项目名称，name是你的实验名称。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 初始化wandb</span>
<span class="kn">import</span> <span class="nn">wandb</span>
<span class="n">wandb</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">project</span><span class="o">=</span><span class="s2">&quot;thorough-pytorch&quot;</span><span class="p">,</span>
           <span class="n">name</span><span class="o">=</span><span class="s2">&quot;wandb_demo&quot;</span><span class="p">,)</span>
</pre></div>
</div>
<p>使用wandb的第二步是设置超参数，这里我们使用wandb.config来设置超参数，这样我们就可以在wandb的界面上看到超参数的变化。wandb.config的使用方法和字典类似，我们可以使用config.key的方式来设置超参数。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 超参数设置</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">wandb</span><span class="o">.</span><span class="n">config</span>  <span class="c1"># config的初始化</span>
<span class="n">config</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>  
<span class="n">config</span><span class="o">.</span><span class="n">test_batch_size</span> <span class="o">=</span> <span class="mi">10</span> 
<span class="n">config</span><span class="o">.</span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">5</span>  
<span class="n">config</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="mf">0.01</span> 
<span class="n">config</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="mf">0.1</span>  
<span class="n">config</span><span class="o">.</span><span class="n">use_cuda</span> <span class="o">=</span> <span class="kc">True</span>  
<span class="n">config</span><span class="o">.</span><span class="n">seed</span> <span class="o">=</span> <span class="mi">2043</span>  
<span class="n">config</span><span class="o">.</span><span class="n">log_interval</span> <span class="o">=</span> <span class="mi">10</span> 

<span class="c1"># 设置随机数</span>
<span class="k">def</span> <span class="nf">set_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">):</span>
    <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span>      
    <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> 
    <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> 

</pre></div>
</div>
<p>第三步是构建训练和测试的pipeline，这里我们使用pytorch的CIFAR10数据集和resnet18来构建训练和测试的pipeline。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">):</span>
    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>

    <span class="k">for</span> <span class="n">batch_id</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span>
        <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
        <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
        <span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>

<span class="c1"># wandb.log用来记录一些日志(accuracy,loss and epoch), 便于随时查看网路的性能</span>
<span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">test_loader</span><span class="p">,</span> <span class="n">classes</span><span class="p">):</span>
    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="n">test_loss</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">example_images</span> <span class="o">=</span> <span class="p">[]</span>

    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">test_loader</span><span class="p">:</span>
            <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
            <span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
            <span class="n">test_loss</span> <span class="o">+=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
            <span class="n">pred</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
            <span class="n">correct</span> <span class="o">+=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
            <span class="n">example_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">wandb</span><span class="o">.</span><span class="n">Image</span><span class="p">(</span>
                <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">caption</span><span class="o">=</span><span class="s2">&quot;Pred:</span><span class="si">{}</span><span class="s2"> Truth:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">classes</span><span class="p">[</span><span class="n">pred</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()],</span> <span class="n">classes</span><span class="p">[</span><span class="n">target</span><span class="p">[</span><span class="mi">0</span><span class="p">]])))</span>

   <span class="c1"># 使用wandb.log 记录你想记录的指标</span>
    <span class="n">wandb</span><span class="o">.</span><span class="n">log</span><span class="p">({</span>
        <span class="s2">&quot;Examples&quot;</span><span class="p">:</span> <span class="n">example_images</span><span class="p">,</span>
        <span class="s2">&quot;Test Accuracy&quot;</span><span class="p">:</span> <span class="mf">100.</span> <span class="o">*</span> <span class="n">correct</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">),</span>
        <span class="s2">&quot;Test Loss&quot;</span><span class="p">:</span> <span class="n">test_loss</span>
    <span class="p">})</span>

<span class="n">wandb</span><span class="o">.</span><span class="n">watch_called</span> <span class="o">=</span> <span class="kc">False</span> 


<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
    <span class="n">use_cuda</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">use_cuda</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
    <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:0&quot;</span> <span class="k">if</span> <span class="n">use_cuda</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
    <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;num_workers&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;pin_memory&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">}</span> <span class="k">if</span> <span class="n">use_cuda</span> <span class="k">else</span> <span class="p">{}</span>

    <span class="c1"># 设置随机数</span>
    <span class="n">set_seed</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">deterministic</span> <span class="o">=</span> <span class="kc">True</span>

    <span class="c1"># 数据预处理</span>
    <span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
    <span class="p">])</span>

    <span class="c1"># 加载数据</span>
    <span class="n">train_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">datasets</span><span class="o">.</span><span class="n">CIFAR10</span><span class="p">(</span>
        <span class="n">root</span><span class="o">=</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span>
        <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="n">transform</span><span class="o">=</span><span class="n">transform</span>
    <span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="n">test_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">datasets</span><span class="o">.</span><span class="n">CIFAR10</span><span class="p">(</span>
        <span class="n">root</span><span class="o">=</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span>
        <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
        <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="n">transform</span><span class="o">=</span><span class="n">transform</span>
    <span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="n">classes</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;plane&#39;</span><span class="p">,</span> <span class="s1">&#39;car&#39;</span><span class="p">,</span> <span class="s1">&#39;bird&#39;</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">,</span> <span class="s1">&#39;deer&#39;</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">,</span> <span class="s1">&#39;frog&#39;</span><span class="p">,</span> <span class="s1">&#39;horse&#39;</span><span class="p">,</span> <span class="s1">&#39;ship&#39;</span><span class="p">,</span> <span class="s1">&#39;truck&#39;</span><span class="p">)</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">lr</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">momentum</span><span class="p">)</span>

    <span class="n">wandb</span><span class="o">.</span><span class="n">watch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">log</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">config</span><span class="o">.</span><span class="n">epochs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
        <span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">)</span>
        <span class="n">test</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">test_loader</span><span class="p">,</span> <span class="n">classes</span><span class="p">)</span>

    <span class="c1"># 本地和云端模型保存</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s1">&#39;model.pth&#39;</span><span class="p">)</span>
    <span class="n">wandb</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;model.pth&#39;</span><span class="p">)</span>


<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
    <span class="n">main</span><span class="p">()</span>

</pre></div>
</div>
<p>当我们运行完上面的代码后，我们就可以在wandb的界面上看到我们的训练结果了和系统的性能指标。同时，我们还可以在setting里面设置训练完给我们发送邮件，这样我们就可以在训练完之后及时的查看训练结果了。
<img alt="" src="../_images/acc_wandb.png" />
<img alt="" src="../_images/wandb_sys.png" />
<img alt="" src="../_images/wandb_config.png" /></p>
<p>我们可以发现，使用wandb可以很方便的记录我们的训练结果，除此之外，wandb还为我们提供了很多的功能，比如：模型的超参数搜索，模型的版本控制，模型的部署等等。这些功能都可以帮助我们更好的管理我们的模型，更好的进行模型的迭代和优化。这些功能我们在后面的更新中会进行介绍。</p>
</section>
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